A Data-Centric Framework for Detecting and Correcting Corrupted Labels

2026-06-10

AI Summary

Real-world data often contain label noise that severely degrades model performance. This work proposes Relabeler, a novel framework that, for the first time, jointly models both local and global relationships among data instances within a unified architecture to enable end-to-end label correction. By leveraging input features and observed labels in concert, Relabeler estimates the most probable clean labels through a data-centric learning paradigm that integrates relational modeling, probabilistic label inference, and end-to-end optimization. Extensive experiments demonstrate that the method consistently outperforms existing approaches across diverse datasets, noise types, and noise rates, achieving up to a 58% improvement in label correction accuracy and a 6% gain in downstream task performance.

Abstract

The performance of machine learning and deep learning models largely depends on the quality of the training data. However, the quality of the real-world datasets is often compromised by noisy labels, which can substantially degrade model accuracy and reliability. To address this challenge, we propose Relabeler, an end-to-end data-centric framework for detecting and correcting corrupted labels. For corrupted label detection, Relabeler jointly leverages both local and global relationships among data instances to identify potentially noisy samples. After detecting suspicious instances, Relabeler further performs label correction by estimating the most probable clean label for each instance based on both its input features and observed noisy label. Extensive experiments across multiple datasets, noise types, and noise rates demonstrate that Relabeler consistently outperforms state-of-the-art baselines, achieving up to 58% improvement in label correction precision and 6% improvement in downstream task performance.

Problem

• noisy labels
• label corruption
• data quality
• label correction
• machine learning

Innovation

• data-centric learning
• noisy label correction
• label noise detection
• end-to-end framework
• reliable machine learning